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Massive Uniform Manipulation: Controlling Large Populations of Simple Robots with a Common Input Signal


Roboticists, biologists, and chemists are now producing large populations of simple robots, but controlling large populations of robots with limited capabilities is difficult, due to communication and onboard-computation constraints. Direct human control of large populations seems even more challenging.

In this paper we investigate control of mobile robots that move in a 2D workspace using three different system models. We focus on a model that uses broadcast control inputs specified in the global reference frame.

In an obstacle-free workspace this system model is uncontrollable because it has only two controllable degrees of freedom---all robots receive the same inputs and move uniformly. We prove that adding a single obstacle can make the system controllable, for any number of robots. We provide a position control algorithm, and demonstrate through extensive testing with human subjects that many manipulation tasks can be reliably completed, even by novice users, under this system model, with performance benefits compared to the alternate models.

We compare the sensing, computation, communication, time, and bandwidth costs for all three system models. Results are validated with extensive simulations and hardware experiments using over 100 robots.

Aaron Becker and Golnaz Habibi and Justin Werfel and Michael Rubenstein and J. McLurkin
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (Accepted)